This review explores how multi-source data-driven machine learning is transforming lung cancer care, from early screening and diagnosis to personalized treatment and prognosis. It examines imaging, multi-omics, liquid biopsy, pathology, and clinical data, alongside traditional and advanced machine learning approaches. By linking data characteristics with model selection and clinical applications, the review highlights opportunities for precision medicine while addressing challenges in data standardization, multimodal integration, interpretability, and effective clinical translation.

Worcester Polytechnic Institute researcher Catherine Whittington is launching a new project to develop laboratory models for the study of uterine fibroids, and before work had even begun, WPI students had already played a critical role.

Researchers in the Riccio College of Engineering at the University of Massachusetts Amherst have demonstrated that redesigning both the hardware and the algorithm can make AI applications on edge devices more efficient. As proof of concept, their system achieved 95.24% accuracy in language identification while reducing computing resources by 90%—the highest reported accuracy from a system of its kind.

Researchers have developed a programmable macrophage engineering strategy that rapidly equips immune cells with pathogen-specific recognition and antibacterial functions through membrane interface modification. The engineered macrophages efficiently capture and eliminate bacteria while simultaneously reshaping the immune microenvironment, providing a promising therapeutic approach for severe bacterial infections occurring under immunosuppressed conditions.

Researchers have developed a cellulose-based hydrogel sensor that maintains stable sensing performance in extreme cold using a zinc chloride and lithium bromide molten salt hydrate system, offering a sustainable and biodegradable platform for next-generation flexible wearable devices and human-machine interfaces. Published in Journal of Bioresources and Bioproducts, the study introduces a binary molten salt hydrate approach to dissolve natural cellulose efficiently within ten minutes. The resulting ionically conductive hydrogel achieves an impressive conductivity of 4.48 S/m and compressive strength of 2.48 MPa. Differential scanning calorimetry confirmed no water crystallization exothermic peaks from -80°C to 20°C, indicating exceptional anti-freezing capability. The material's shear-thinning properties further enable 3D printing of customized sensor geometries for human-machine interfaces.